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Record W4396592081 · doi:10.2514/1.j063384

Evaluating an Additive Manufactured Acoustic Metamaterial Using the Advanced Noise Control Fan

2024· article· en· W4396592081 on OpenAlexaff
Eoghan P. Ross, Kelvin M. Figueroa-Ibrahim, Scott Morris, Daniel L. Sutliff, Gareth J. Bennett

Bibliographic record

VenueAIAA Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsTrinity College
FundersHorizon 2020 Framework ProgrammeGlenn Research CenterEuropean CommissionNational Aeronautics and Space Administration
KeywordsNoise reductionNoise controlNacelleAcousticsBroadbandNoise (video)MetamaterialEngineeringReduction (mathematics)AeroacousticsComputer scienceTelecommunicationsMaterials scienceAerospace engineeringSound pressurePhysicsTurbine

Abstract

fetched live from OpenAlex

This paper examines the performance of a 3D printed acoustic metamaterial as an acoustic treatment for aircraft engine nacelles in the Advanced Noise Control Fan. As the level of air travel continues to increase, so too does the demand for better noise-reduction technologies for aircraft. Engines are one of the two main sources of noise generated by aircraft, with fan noise, in particular, being of concern due to its broadband and tonal contributions. Small and lightweight methods of addressing both broadband and tonal noise are necessary due to the limitations presented by the current engine design. Presented in this paper is a novel acoustic metamaterial that has undergone design optimization for broadband noise reduction. The final design was produced using 3D printing and tested using the Advanced Noise Control Fan at the University of Notre Dame. It was found that the material is capable of reducing the first harmonic of the blade passing frequency by up to 18.5 dB, with an overall noise reduction of 3.7 dB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.350
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2024
Admission routes1
Has abstractyes

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